The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
ANDRUBIS -- 1,000,000 Apps Later: A View on Current Android Malware Behaviors
Abstract
Android is the most popular smartphone operating system with a market share of 80%, but as a consequence, also the platform most targeted by malware. To deal with the increasing number of malicious Android apps in the wild, malware analysts typically rely on analysis tools to extract characteristic information about an app in an automated fashion. While the importance of such tools has been addressed by the research community, the resulting prototypes remain limited in terms of analysis capabilities and availability. In this paper we present ANDRUBIS, a fully automated, publicly available and comprehensive analysis system for Android apps. ANDRUBIS combines static analysis with dynamic analysis on both Dalvik VM and system level, as well as several stimulation techniques to increase code coverage. With ANDRUBIS, we collected a dataset of over 1,000,000 Android apps, including 40% malicious apps. This dataset allows us to discuss trends in malware behavior observed from apps dating back as far as 2010, as well as to present insights gained from operating ANDRUBIS as a publicly available service for the past two years.
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Cites
Drebin: Effective and Explainable Detection of Android Malware in Your Pocket
Arp, D., Spreitzenbarth, M., Hübner, M., Gascon, H., & Rieck, K. (2014) · Proceedings 2014 Network and Distributed System Security Symposium · doi:10.14722/ndss.2014.23247 · Cited by 2,331
TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones
Enck, W., Gilbert, P., Chun, B.-G., Cox, L. P., Jung, J., McDaniel, P. D., & Sheth, A. N. (2010) · doi:10.5555/1924943.1924971 · Cited by 2,238
Dissecting Android Malware: Characterization and Evolution
Zhou, Y., & Jiang, X. (2012) · 2012 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2012.16 · Cited by 2,133
Android permissions demystified
Felt, A. P., Chin, E., Hanna, S., Song, D., & Wagner, D. (2011) · Proceedings of the 18th ACM conference on Computer and communications security · doi:10.1145/2046707.2046779 · Cited by 1,291
A survey of mobile malware in the wild
Felt, A. P., Finifter, M., Chin, E., Hanna, S., & Wagner, D. (2011) · Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices · doi:10.1145/2046614.2046618 · Cited by 768
Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets
Zhou, Y., Wang, Z., Zhou, W., & Jiang, X. (2012) · Network and Distributed System Security Symposium · Cited by 734
PScout: analyzing the Android permission specification
Au, K. W. Y., Zhou, Y. F., Huang, Z., & Lie, D. (2012) · Proceedings of the 2012 ACM conference on Computer and communications security · doi:10.1145/2382196.2382222 · Cited by 644
Scalable, behavior-based malware clustering
Bayer, U. (2009) · Cited by 603
Detecting repackaged smartphone applications in third-party android marketplaces
Zhou, W., Zhou, Y., Jiang, X., & Ning, P. (2012) · Proceedings of the second ACM conference on Data and Application Security and Privacy · doi:10.1145/2133601.2133640 · Cited by 590
An Android Application Sandbox system for suspicious software detection
Bläsing, T., Batyuk, L., Schmidt, A.-D., Camtepe, S. A., & Albayrak, S. (2010) · 2010 5th International Conference on Malicious and Unwanted Software · doi:10.1109/malware.2010.5665792 · Cited by 452
Cited by
Significant Permission Identification for Machine-Learning-Based Android Malware Detection
Li, J., Sun, L., Yan, Q., Li, Z., Srisa-an, W., & Ye, H. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2789219 · Cited by 607
Android Security: A Survey of Issues, Malware Penetration, and Defenses
Faruki, P., Bharmal, A., Laxmi, V., Ganmoor, V., Gaur, M. S., Conti, M., & Rajarajan, M. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2386139 · Cited by 559
MalDozer: Automatic framework for android malware detection using deep learning
Karbab, E. B., Debbabi, M., Derhab, A., & Mouheb, D. (2018) · Digital Investigation · doi:10.1016/j.diin.2018.01.007 · Cited by 403
Deep Ground Truth Analysis of Current Android Malware
Wei, F., Li, Y., Roy, S., Ou, X., & Zhou, W. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-60876-1_12 · Cited by 398
FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic
van Ede, T., Bortolameotti, R., Continella, A., Ren, J., Dubois, D. J., Lindorfer, M., Choffnes, D., van Steen, M., & Peter, A. (2020) · Proceedings 2020 Network and Distributed System Security Symposium · doi:10.14722/ndss.2020.24412 · Cited by 339
Self-citation
Metadata
- Authors
- Lindorfer, M., Neugschwandtner, M., Weichselbaum, L., Fratantonio, Y., Veen, V. V. D., & Platzer, C.
- Venue
- 2014 Third International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS)
- Publisher
- IEEE
- Published
- 2014-09
- Pages
- 3-17
- OpenAlex
- W2334842536
- Cited by
- 304
- References
- 27
- Type
- dataset
- Language
- en
- Open access
- closed
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- Lindorfer, M., Neugschwandtner, M., Weichselbaum, L., Fratantonio, Y., Veen, V. V. D., & Platzer, C. (2014). ANDRUBIS -- 1,000,000 Apps Later: A View on Current Android Malware Behaviors. 2014 Third International Workshop on Building Analysis Datasets and Gathering Experience Returns for Security (BADGERS). https://doi.org/10.1109/badgers.2014.7
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